The present invention relates to computing networks and, more particularly, to a machine-learning optimization of networking based upon a multitude of performance metrics.
Current computing networks and their associated networking equipment are statically built to support the humanly estimated need of a given application or throughput requirement. They are also built in a very proprietary manner, thus forcing the user to use one service provider for all processes.
As a result, current computing networks and their associated networking equipment lack elasticity. While many advancements have begun to provide and “on demand” functionality, they do not solve for the current constraints on the flow of traffic through the network. Specifically, networking performance capable devices today are limited by simple routing protocols that are over a decade old. Moreover, the selection of routing or path is based on a per device basis, typically requiring proprietary licenses, and otherwise only working on devices of the same manufacturer.
In private-network services, such as MPLS or VPLS, there are Quality of Service capabilities that classify and prioritize traffic within the Wide Area Network Service Providers network, but this is completely within the confines of their completely owned and privatized networks. Furthermore, such private networks are predefined and weighted routes or paths built on a pre-constructed network.
The global Internet provides and even larger problem. Not one service provider can control or guarantee the end to end throughput and security of traffic across the open Internet. Some Internet provides provide improved caching and TCP optimization tactics, but the impact is minimal. This is what keeps customers loyal to their chosen private service provider networks; however, the drawback to loyalty is that Internet service providers do not rely on competitor networks for support during issues like congestion or outage.
Other networking platforms are completely proprietary. They rely on existing WAN and Internet infrastructure to follow pre-defined rules, not dynamic or learned applications. And so, if there is congestion on the internet many VPN or IPSec platforms will not see and are not able to dynamically react to congestion, packet loss, jitter, or latency. To date, VPN services see only two points, the origin and destination. As a result, high costs and complex configuration make these platforms cumbersome and undesirable.
All other devices that use VPNs, IPSec, GRE, Auto VPN, or any other site to site topologies, do not control the performance, latency, speed or quality of the entire end to end Internet path. Some control the selection of what local circuit to traverse, but have no control beyond that.
The issue of flow traffic through networks will only be acerbated by the expectations of today's data consumers, who demand the following: elasticity in their networks (when it is hard to predict network needs, for example: twitter, events or seasonal changes); configurability control (consumers want to do it ourselves; and now!); data security made easy and safe (even though security has become tougher and more complex to address); visibility of performance and instant reporting (i.e., consumer wants to know if they are getting what they purchased); and price compression paired with an increase in capacity and improvement of performance.
As can be seen, there is a need for a machine-learning optimization of networking based upon a multitude of performance metrics, also or at times impacting equipment configurations. The optimization of traffic flow of the present invention is delivered through the creation of data flows and device profiles using machine learning adapted to provide real-time application. Unlike other service providers or equipment manufacturers today, the present invention empowers the customer to deploy and manage their domestic and global networks and associated equipment agnostically, yet securely and instantly with little or no communications from the provider of the present invention, and where consumers can also remove or disconnect at their leisure.
The present invention provides a platform that empowers the user to instantly stand up a secure and reliable domestic or global network, thereby bypassing the “brick and mortar” service provider model and lengthy timeframes to deploy. The empowering software of the present invention may be deployed in a plurality of data centers across the globe, wherein software defined gateways may be powered by machine learning artificial intelligence. The software of the present invention can also be deployed on local and on-site equipment as well.
The platform utilizes the collected performance data via a machine learning module to enhance performance and reduce the latency within the Internet, MPLS, VPLS and EoIP networks based on knowledge of the most common destination so as to create the best latency-based optimized route to minimize delay for real-time traffic, adding significant value to the WAN core traffic engineering and RSVP TE management.
Furthermore, the platform provides stronger security through the traffic behavior profiles that protect the user from anomalies by alerting the user to change in traffic patterns which could be caused by malware, a virus, or an unauthorized access.
The software of the present invention is adapted to establish a core network among a plurality of deployed data centers. In certain embodiments, endpoints interconnecting the core network with a plurality of Internet software provides, based upon in part a L2TP over IPSec negotiation, a machine learning module, which has taken into account thousands of real-time and historic metrics within less than two seconds. Through this process of optimizing Internet-based networks based upon real time and historic machine learned performance metrics, Internet service provider(s) are selected as optimal within the path of the initial connection for navigating the specific path of data as is passes through such service providers.
Once connected to the core network, the customer may be provided with options of stand up traditional services, such as MPLS, VPLS, and EoIP, over a completely optimized machine learning core network, leveraging L2TP over IPSec among the plurality of data center connections.
Based on the machine learning module provided through the platform a flow of data may route over 1, 2, 5, or 10+ different Internet service providers based purely upon obtaining the optimal performing route/path. This is a combination of IP transit connectivity coupled with the machine learning functionality, creating an artificially intelligent network.
The collected data predicts the growth in bandwidth requirements that is based upon current and past performance. This enables the planning ability for future expansion in hardware and software requirement.
The platform of the present invention is intuitive and easy to use as it predicts and executes performance enhancement and scalability functions. The platform provides an onramp to a customer evolving to full SDN (Software Defined Networking). Customers can use a variety of existing hardware and accomplish centralized SDN control and visibility through the use of our software.